Developing AI-powered microclimate models and yield forecasting tools for climate-smart agroforestry in Germany

Tree-crop interactions in agroforestry systems generate spatial microclimate heterogeneity that significantly influences crop yield and quality. Yet, predictive understanding of these fine-scale dynamics remains limited. This study introduces a data-driven framework to quantify and forecast microclimate variability and its effects on barley performance in a temperature alley cropping system. A high-resolution IoT sensor network was used to monitor air temperature, humidity, vapor pressure, solar radiation, wind speed, and soil moisture and temperature (15–60 cm) across four distances (1, 4, 7, and 24 m) and two orientations (leeward and windward) from tree strips. Results revealed that proximity to trees reduced air temperature by up to 1.2 °C, lowered vapor pressure deficit by 0.4 kPa, and increased soil moisture at 15 cm by 20 %. These microclimatic modifications significantly impacted crop performance: dry matter yield was highest at 24 m (4.8 t/ha), while crude protein content peaked at intermediate distances (1–7 m), where moderate stress and sufficient light co-occurred. Deep learning Long Short-Term Memory (LSTM) models accurately predict microclimate variables from macroclimate inputs, with R² ranging from 0.73 to 0.98. An AI agent was deployed via a public dashboard to support real-time, user-driven exploration of forecasts and enhance site-specific agroforestry decision-making. This study highlights the vital role of microclimate in shaping agroforestry outcomes and demonstrates how IoT-based sensing and AI tools can improve prediction and management. Future research should expand to multi-year, multi-site datasets and integrate biophysical traits, remote sensing, and process-based models such as Hi-sAFe for broader applicability.